This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
| ISBN: | 9783662501900 |
| Publication date: | 3rd September 2016 |
| Author: | YenWei Chen, L C Jain |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 199 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Maths for engineers Pattern recognition Artificial intelligence |
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
Subspace Methods for Pattern Recognition in Intelligent Environment features in the following genres: Maths for engineers, Pattern recognition, Artificial intelligence
Subspace Methods for Pattern Recognition in Intelligent Environment is available in Paperback, Hardback
Subspace Methods for Pattern Recognition in Intelligent Environment was written by YenWei Chen, L C Jain and published by Springer an imprint of Springer Berlin Heidelberg
Subspace Methods for Pattern Recognition in Intelligent Environment has 199 pages
Yes it is part of Studies in Computational Intelligence series